Ready, Steady, Go AI: A practical tutorial on fundamentals of artificial intelligence and its applications in phenomics image analysis

Nakhle, Farid; Harfouche, Antoine

doi:10.1016/j.patter.2021.100323

Cited by 23 publications

(7 citation statements)

References 115 publications

(177 reference statements)

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“…Hypocotyl UNet by Dobos et al ( 2019 ) is another publicly available tool that can estimate the hypocotyl length in seedlings and can be adapted for different datasets. Nakhle and Harfouche ( 2021 ) developed an interactive tutorial with open-source libraries to analyze plant phenotyping data using deep learning models. The authors believe that the tool can benefit early career researchers and students in extracting biologically meaningful information from deep learning models.…”

Section: Explainable Ai and Plant Phenotypingmentioning

confidence: 99%

Explainable deep learning in plant phenotyping

Mostafa,

Mondal,

Panjvani

et al. 2023

Front. Artif. Intell.

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The increasing human population and variable weather conditions, due to climate change, pose a threat to the world's food security. To improve global food security, we need to provide breeders with tools to develop crop cultivars that are more resilient to extreme weather conditions and provide growers with tools to more effectively manage biotic and abiotic stresses in their crops. Plant phenotyping, the measurement of a plant's structural and functional characteristics, has the potential to inform, improve and accelerate both breeders' selections and growers' management decisions. To improve the speed, reliability and scale of plant phenotyping procedures, many researchers have adopted deep learning methods to estimate phenotypic information from images of plants and crops. Despite the successful results of these image-based phenotyping studies, the representations learned by deep learning models remain difficult to interpret, understand, and explain. For this reason, deep learning models are still considered to be black boxes. Explainable AI (XAI) is a promising approach for opening the deep learning model's black box and providing plant scientists with image-based phenotypic information that is interpretable and trustworthy. Although various fields of study have adopted XAI to advance their understanding of deep learning models, it has yet to be well-studied in the context of plant phenotyping research. In this review article, we reviewed existing XAI studies in plant shoot phenotyping, as well as related domains, to help plant researchers understand the benefits of XAI and make it easier for them to integrate XAI into their future studies. An elucidation of the representations within a deep learning model can help researchers explain the model's decisions, relate the features detected by the model to the underlying plant physiology, and enhance the trustworthiness of image-based phenotypic information used in food production systems.

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Section: Explainable Ai and Plant Phenotypingmentioning

confidence: 99%

Explainable deep learning in plant phenotyping

Mostafa,

Mondal,

Panjvani

et al. 2023

Front. Artif. Intell.

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“…Researchers are increasingly turning to new algorithms and data analysis workflows, often developed using free programming environments like Python or Octave. Open-source projects like Orange data mining software provide standardized data processing workflows for various machine learning problems, streamlining data processing and analysis (Nakhle et al 2021).…”

Section: Artificial Intelligencementioning

confidence: 99%

State-of-the-art non-destructive approaches for maturity index determination in fruits and vegetables: principles, applications, and future directions

Anjali,

Jena,

Bamola

et al. 2024

Food Prod Process and Nutr

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Recent advancements in signal processing and computational power have revolutionized computer vision applications in diverse industries such as agriculture, food processing, biomedical, and the military. These developments are propelling efforts to automate processes and enhance efficiency. Notably, computational techniques are replacing labor-intensive manual methods for assessing the maturity indices of fruits and vegetables during critical growth stages.This review paper focuses on recent advancements in computer vision techniques specifically applied to determine the maturity indices of fruits and vegetables within the food processing sector. It highlights successful applications of Nuclear Magnetic Resonance (NMR), Near-Infrared Spectroscopy (NIR), thermal imaging, and image scanning. By examining these techniques, their underlying principles, and practical feasibility, it offers valuable insights into their effectiveness and potential widespread adoption. Additionally, integrating biosensors and AI techniques further improves accuracy and efficiency in maturity index determination.In summary, this review underscores the significant role of computational techniques in advancing maturity index assessment and provides insights into their principles and effective utilization. Looking ahead, the future of computer vision techniques holds immense potential. Collaborative efforts among experts from various fields will be crucial to address challenges, ensure standardization, and safeguard data privacy. Embracing these advancements can lead to sustainable practices, optimized resource management, and progress across industries. Graphical Abstract

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“…Recently there have been articles in multiple domains walking a domain expert through the best tools and techniques available to employ ML [52,75,111,66,92]. For example, Nakhle and Harfouche provide four detailed Jupyter notebooks 52 walking domain experts in phenomics (plant sciences) through four steps of a ML task [82].…”

Section: Guiding the Domain Expertmentioning

confidence: 99%

Building Domain-Specific Machine Learning Workflows: A Conceptual Framework for the State-of-the-Practice

Oakes¹,

Famelis²,

Sahraoui³

2022

Preprint

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Domain experts are increasingly employing machine learning to solve their domain-specific problems. This article presents six key challenges that a domain expert faces in transforming their problem into a computational workflow, and then into an executable implementation. These challenges arise out of our conceptual framework which presents the "route" of options that a domain expert may choose to take while developing their solution.To ground our conceptual framework in the stateof-the-practice, this article discusses a selection of available textual and graphical workflow systems and their support for these six challenges. Case studies from the literature in various domains are also examined to highlight the tools used by the domain experts as well as a classification of the domain-specificity and machine learning usage of their problem, workflow, and implementation.The state-of-the-practice informs our discussion of the six key challenges, where we identify which challenges are not sufficiently addressed by available tools. We also suggest possible research directions for software engineering researchers to increase the automation of these tools and disseminate best-practice techniques between software engineering and various scientific domains.

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Ready, Steady, Go AI: A practical tutorial on fundamentals of artificial intelligence and its applications in phenomics image analysis

Cited by 23 publications

References 115 publications

Explainable deep learning in plant phenotyping

Explainable deep learning in plant phenotyping

State-of-the-art non-destructive approaches for maturity index determination in fruits and vegetables: principles, applications, and future directions

Building Domain-Specific Machine Learning Workflows: A Conceptual Framework for the State-of-the-Practice

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